Manuscript Topics

Descriptive analytics is the process of using current and historical data to identify patterns and relationships. Used in healthcare, it seeks to analyze phenomena trying to describe what has really happened, posing the basis to reflect on the correlation with possible causes. Nonetheless, a meticulous scrutiny of vast patient data pools can extract valuable information for identyfying connections and uncover unnoticed roots of give events, but it cannot alone help in forecasting future events, like providing more accurate diagnoses, anticipating disease outbreaks, programing the reductionof the cost of treatments, without the intervention of predictive modelling. In turn, predictive modelling for healthcare should not just follow rigid and pre-arranged computational rules, but it should be able both to learn from data and to experiment with unconventional models to become better at making decisions. To deliver data-based quality care, accurate diagnosis and personalized treatments needs not only recognizing historical trends but also predicting with more accuracy future outcomes. In fact, the more information is encountered, the more a holistic process should be supported, where the prediction becomes the final synthesis of the broad spectrum of various medical factors and their interplay between the individual, the population and the environment.

This special issue is dedicated to presenting innovative models in the field of predictive analytics for healthcare. Articles are invited in topics such as predictive computational analytics for healthcare and predictive health modeling based on AI, independently of the medical field of application. Authors are invited to submit both original research and survey papers reporting the newest results in the field, with no restriction on the length of the submitted papers.

Topics of interest include but are not be limited to:
• Applied probability and statistics for predictive modeling in healthcare
• Parametric, non-parametric and semiparametric modeling for predictive healthcare
• Operations Research, exact-approximate optimization for predictive health modelling
• Computational intelligence for predictive health analytics
• Unconventional computational models for predictive analytics
• Uplift modelling for predictive healthcare
• AI, Big data, machine learning and deep learning for predictive healthcare
• Intelligent biomedicine, infodemiology, bio and immuno -informatics for predicting health

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